{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "keras_02_mnist_dense.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.3"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-mnist-tutorial/keras_02_mnist_dense.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lvo0t7XVIkWZ"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cCpkS9C_H7Tl",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 128\n",
        "EPOCHS = 10\n",
        "\n",
        "training_images_file   = 'gs://mnist-public/train-images-idx3-ubyte'\n",
        "training_labels_file   = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
        "validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
        "validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AoilhmYe1b5t",
        "outputId": "f709e6c4-ad6f-4451-8584-d7a42e92f364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os, re, math, json, shutil, pprint\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import IPython.display as display\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "tf.enable_eager_execution()\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version 1.14.0-rc1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "qhdz68Xm3Z4Z",
        "colab": {}
      },
      "source": [
        "#@title visualization utilities [RUN ME]\n",
        "\"\"\"\n",
        "This cell contains helper functions used for visualization\n",
        "and downloads only. You can skip reading it. There is very\n",
        "little useful Keras/Tensorflow code here.\n",
        "\"\"\"\n",
        "\n",
        "# Matplotlib config\n",
        "plt.ioff()\n",
        "plt.rc('image', cmap='gray_r')\n",
        "plt.rc('grid', linewidth=1)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0', figsize=(16,9))\n",
        "# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "\n",
        "# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
        "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
        "  \n",
        "  # get one batch from each: 10000 validation digits, N training digits\n",
        "  batch_train_ds = training_dataset.apply(tf.data.experimental.unbatch()).batch(N)\n",
        "  \n",
        "  # eager execution: loop through datasets normally\n",
        "  if tf.executing_eagerly():\n",
        "    for validation_digits, validation_labels in validation_dataset:\n",
        "      validation_digits = validation_digits.numpy()\n",
        "      validation_labels = validation_labels.numpy()\n",
        "      break\n",
        "    for training_digits, training_labels in batch_train_ds:\n",
        "      training_digits = training_digits.numpy()\n",
        "      training_labels = training_labels.numpy()\n",
        "      break\n",
        "    \n",
        "  else:\n",
        "    v_images, v_labels = validation_dataset.make_one_shot_iterator().get_next()\n",
        "    t_images, t_labels = batch_train_ds.make_one_shot_iterator().get_next()\n",
        "    # Run once, get one batch. Session.run returns numpy results\n",
        "    with tf.Session() as ses:\n",
        "      (validation_digits, validation_labels,\n",
        "       training_digits, training_labels) = ses.run([v_images, v_labels, t_images, t_labels])\n",
        "  \n",
        "  # these were one-hot encoded in the dataset\n",
        "  validation_labels = np.argmax(validation_labels, axis=1)\n",
        "  training_labels = np.argmax(training_labels, axis=1)\n",
        "  \n",
        "  return (training_digits, training_labels,\n",
        "          validation_digits, validation_labels)\n",
        "\n",
        "# create digits from local fonts for testing\n",
        "def create_digits_from_local_fonts(n):\n",
        "  font_labels = []\n",
        "  img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
        "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
        "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
        "  d = PIL.ImageDraw.Draw(img)\n",
        "  for i in range(n):\n",
        "    font_labels.append(i%10)\n",
        "    d.text((7+i*28,0 if i<10 else -4), str(i%10), fill=(255,255), font=font1 if i<10 else font2)\n",
        "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
        "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
        "  return font_digits, font_labels\n",
        "\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_digits(digits, predictions, labels, title, n):\n",
        "  fig = plt.figure(figsize=(13,3))\n",
        "  digits = np.reshape(digits, [n, 28, 28])\n",
        "  digits = np.swapaxes(digits, 0, 1)\n",
        "  digits = np.reshape(digits, [28, 28*n])\n",
        "  plt.yticks([])\n",
        "  plt.xticks([28*x+14 for x in range(n)], predictions)\n",
        "  plt.grid(b=None)\n",
        "  for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
        "    if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
        "  plt.imshow(digits)\n",
        "  plt.grid(None)\n",
        "  plt.title(title)\n",
        "  display.display(fig)\n",
        "  \n",
        "# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
        "def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
        "  idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
        "  for i in range(lines):\n",
        "    display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
        "                   \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)\n",
        "\n",
        "def plot_learning_rate(lr_func, epochs):\n",
        "  xx = np.arange(epochs+1, dtype=np.float)\n",
        "  y = [lr_decay(x) for x in xx]\n",
        "  fig, ax = plt.subplots(figsize=(9, 6))\n",
        "  ax.set_xlabel('epochs')\n",
        "  ax.set_title('Learning rate\\ndecays from {:0.3g} to {:0.3g}'.format(y[0], y[-2]))\n",
        "  ax.minorticks_on()\n",
        "  ax.grid(True, which='major', axis='both', linestyle='-', linewidth=1)\n",
        "  ax.grid(True, which='minor', axis='both', linestyle=':', linewidth=0.5)\n",
        "  ax.step(xx,y, linewidth=3, where='post')\n",
        "  display.display(fig)\n",
        "\n",
        "class PlotTraining(tf.keras.callbacks.Callback):\n",
        "  def __init__(self, sample_rate=1, zoom=1):\n",
        "    self.sample_rate = sample_rate\n",
        "    self.step = 0\n",
        "    self.zoom = zoom\n",
        "    self.steps_per_epoch = 60000//BATCH_SIZE\n",
        "\n",
        "  def on_train_begin(self, logs={}):\n",
        "    self.batch_history = {}\n",
        "    self.batch_step = []\n",
        "    self.epoch_history = {}\n",
        "    self.epoch_step = []\n",
        "    self.fig, self.axes = plt.subplots(1, 2, figsize=(16, 7))\n",
        "    plt.ioff()\n",
        "\n",
        "  def on_batch_end(self, batch, logs={}):\n",
        "    if (batch % self.sample_rate) == 0:\n",
        "      self.batch_step.append(self.step)\n",
        "      for k,v in logs.items():\n",
        "        # do not log \"batch\" and \"size\" metrics that do not change\n",
        "        # do not log training accuracy \"acc\"\n",
        "        if k=='batch' or k=='size':# or k=='acc':\n",
        "          continue\n",
        "        self.batch_history.setdefault(k, []).append(v)\n",
        "    self.step += 1\n",
        "\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    plt.close(self.fig)\n",
        "    self.axes[0].cla()\n",
        "    self.axes[1].cla()\n",
        "      \n",
        "    self.axes[0].set_ylim(0, 1.2/self.zoom)\n",
        "    self.axes[1].set_ylim(1-1/self.zoom/2, 1+0.1/self.zoom/2)\n",
        "    \n",
        "    self.epoch_step.append(self.step)\n",
        "    for k,v in logs.items():\n",
        "      # only log validation metrics\n",
        "      if not k.startswith('val_'):\n",
        "        continue\n",
        "      self.epoch_history.setdefault(k, []).append(v)\n",
        "\n",
        "    display.clear_output(wait=True)\n",
        "    \n",
        "    for k,v in self.batch_history.items():\n",
        "      self.axes[0 if k.endswith('loss') else 1].plot(np.array(self.batch_step) / self.steps_per_epoch, v, label=k)\n",
        "      \n",
        "    for k,v in self.epoch_history.items():\n",
        "      self.axes[0 if k.endswith('loss') else 1].plot(np.array(self.epoch_step) / self.steps_per_epoch, v, label=k, linewidth=3)\n",
        "      \n",
        "    self.axes[0].legend()\n",
        "    self.axes[1].legend()\n",
        "    self.axes[0].set_xlabel('epochs')\n",
        "    self.axes[1].set_xlabel('epochs')\n",
        "    self.axes[0].minorticks_on()\n",
        "    self.axes[0].grid(True, which='major', axis='both', linestyle='-', linewidth=1)\n",
        "    self.axes[0].grid(True, which='minor', axis='both', linestyle=':', linewidth=0.5)\n",
        "    self.axes[1].minorticks_on()\n",
        "    self.axes[1].grid(True, which='major', axis='both', linestyle='-', linewidth=1)\n",
        "    self.axes[1].grid(True, which='minor', axis='both', linestyle=':', linewidth=0.5)\n",
        "    display.display(self.fig)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lz1Zknfk4qCx"
      },
      "source": [
        "### tf.data.Dataset: parse files and prepare training and validation datasets\n",
        "Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZE8dgyPC1_6m",
        "colab": {}
      },
      "source": [
        "AUTO = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "def read_label(tf_bytestring):\n",
        "    label = tf.decode_raw(tf_bytestring, tf.uint8)\n",
        "    label = tf.reshape(label, [])\n",
        "    label = tf.one_hot(label, 10)\n",
        "    return label\n",
        "  \n",
        "def read_image(tf_bytestring):\n",
        "    image = tf.decode_raw(tf_bytestring, tf.uint8)\n",
        "    image = tf.cast(image, tf.float32)/256.0\n",
        "    image = tf.reshape(image, [28*28])\n",
        "    return image\n",
        "  \n",
        "def load_dataset(image_file, label_file):\n",
        "    imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
        "    imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
        "    labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
        "    labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
        "    dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
        "    return dataset \n",
        "  \n",
        "def get_training_dataset(image_file, label_file, batch_size):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache()  # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
        "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    dataset = dataset.batch(batch_size, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
        "    dataset = dataset.prefetch(AUTO)  # fetch next batches while training on the current one (-1: autotune prefetch buffer size)\n",
        "    return dataset\n",
        "  \n",
        "def get_validation_dataset(image_file, label_file):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache() # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
        "    dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    return dataset\n",
        "\n",
        "# instantiate the datasets\n",
        "training_dataset = get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file)\n",
        "\n",
        "# For TPU, we will need a function that returns the dataset\n",
        "training_input_fn = lambda: get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_input_fn = lambda: get_validation_dataset(validation_images_file, validation_labels_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_fXo6GuvL3EB"
      },
      "source": [
        "### Let's have a look at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yZ4tjPKvL2eh",
        "colab": {}
      },
      "source": [
        "N = 24\n",
        "(training_digits, training_labels,\n",
        " validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
        "display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
        "display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
        "font_digits, font_labels = create_digits_from_local_fonts(N)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KIc0oqiD40HC"
      },
      "source": [
        "### Keras model\n",
        "If you are not sure what cross-entropy, dropout, softmax or batch-normalization mean, head here for a crash-course: [Tensorflow and deep learning without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/#featured-code-sample)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "56y8UNFQIVwj",
        "outputId": "9da02b39-5e60-4474-816f-f76376a9ad58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "model = tf.keras.Sequential(\n",
        "  [\n",
        "      tf.keras.layers.Input(shape=(28*28,)),\n",
        "      tf.keras.layers.Dense(200, activation='relu'),\n",
        "      tf.keras.layers.Dense(60, activation='relu'),\n",
        "      tf.keras.layers.Dense(10, activation='softmax')\n",
        "  ])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# print model layers\n",
        "model.summary()\n",
        "\n",
        "# utility callback that displays training curves\n",
        "plot_training = PlotTraining(sample_rate=10, zoom=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 200)               157000    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 60)                12060     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                610       \n",
            "=================================================================\n",
            "Total params: 169,670\n",
            "Trainable params: 169,670\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CuhDh8ao8VyB"
      },
      "source": [
        "### Train and validate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TTwH_P-ZJ_xx",
        "outputId": "5e3d020d-7995-48db-e749-f445e1e3d105",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "steps_per_epoch = 60000//BATCH_SIZE  # 60,000 items in this dataset\n",
        "print(\"Steps per epoch: \", steps_per_epoch)\n",
        "\n",
        "history = model.fit(training_dataset, steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
        "                    validation_data=validation_dataset, validation_steps=1, callbacks=[plot_training])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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47Gc/i4suugj9/f245ZZb8NGPfjTQZ47VL8ejmXbdEyL3f5H6I8WxK1OsYMW6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\ny2AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r468/468 [==============================] - 3s 7ms/step - loss: 0.0164 - acc: 0.9948 - val_loss: 0.0846 - val_acc: 0.9770\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w12OId8Mz7dF",
        "outputId": "aab94d2d-f018-45a5-881d-de27225be66d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "source": [
        "# recognize digits from local fonts\n",
        "probabilities = model.predict(font_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_digits(font_digits, predicted_labels, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
        "\n",
        "# recognize validation digits\n",
        "probabilities = model.predict(validation_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HkP/4CZp06wqVQgFVaSkkFhYQ6esj48xZvDh3gfs/2w/6ImXPX8h/+AhlRUV4tOq3Gssu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\nNED+P2yiVySrWZv2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lvr6+vMiRJzs7m7Kzs8na2poAkKenJ61YsYL8/f1p9+7dVFlZyXse/o0cOXKE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\n/wMwvKWXaLldfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ynj17hgULFgjbKUu96dWtW7fAcXW5psUcCNUX6jExMc1mK8rLy9PzgHIcJ9ou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\nDAaDwWAwGIx2yP8H8D94DYCkzCsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sLISfnx8cHR1x7do1XLt2zSjXoS3UtdNBxsfHC9ui86EKLQmDOXnyZJ0hPRs3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\nDAaD0Qr5P1xDs3xolS1XAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sWTRr1iyDxtdUVKjnURfm1mhrqHdGUVERubm5abVtZ5SWlqo5jX744Qe9y9IH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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rs8HBwaJcZ03ExcXVS6ifO3fOZKG+Y8cOunPnDm3ZskVYTZnjOEpOTqaysjJa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\nYII=\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ndT6XXnZ+fn4G55o3BSmFIBFh/fr1itqqqKjAlClTMGXKFB4MHRUVpdj5/uqr\nr3jHrE+fPnp2Ll68yDt7Wq0W8fHxeiPvpvrqFxQU8JWGb968ifj4eH6era2tzTZ70xCSf+umTZuM\n+v3XX38NlUrV5KxPZWUlX1DDw8ND8RfBjRs34OHhAX9//zrBd1OmTIFKpcKaNWuwZs2aZu9b8kPX\n9YHu378/du7cyYNFX3nlFb3sN97e3vD29pbr8HhmMCLCyZMnm/x+RkaGXuKBppA6M76+vtxft3YJ\nDw/nmSmkDkrtEXW5VmyunaZOEhSNCeT6li2Xk9LSUvTs2VN2oW4IknCRAk+ViCn64osvYG1tDTc3\nN7i5uSmei78+rl+/zhcYfO+998AYMyh+zhh0A6QNScEoZWTZsGFDs+ycPXsWI0eOrLdDb0ixsrLi\nHX853tW1hTpjTPZkFs1FV6hLo+qNjaxbZEQ9Li4Oe/fuNTjIq6ysDBEREQgICDDpZjp16hQYY9iz\nZ0+D3ykuLkZcXBzs7e3x0UcfGW2ruULdy8sLRUVFWLZsmclBSps2bUKbNm3g6+tbx8VBWnp9wYIF\nsi/FrMvt27cbvSnMLdSlwFylhXpBQQGCg4P1glcMnco2ls8//xyff/45GGMGBYq6urry8y5HUKs0\nkimlnZTKlClTTN63qeTm5sLOzs7o1G7SC6cpoS5lTWDM8MV+jCU/Px/t2rWDSqXCggUL6nw+a9Ys\nqFQq9OnTB3369GnWvouLi/Hqq6/i1VdfBWM1C+ocOHCAvzhVKhXi4uLw/PlzveBlabG02sGuxlJU\nVIQePXqAiDBkyJBGn1Wpqal1cqw3hTS6qDs63tDfhj6T0y3g1q1bPJhUd3R9xIgRiI6O5tlIJJKT\nk/Xy2Csh1H/77TcwxtC5c2ezL0x4+vRpnD59mh+XEu6RUu70UaNGYdSoUbLvvzaVlZXIyclBTk4O\n9u3bh169eqFt27bw8vLigx2MMfz888+K2E9NTeWj5FLn++jRo/UOXBYWFiIgIABE1CxXth9++KFe\nFzlDS2xsrKyBy0CNB0dtO0otDPf8+XM+cNYQhw4dQvfu3fXqIwXxNoRFhHpzuXTpkmyp3tzc3DB6\n9Og626VlhIOCgoxaEro20dHRsLW1xf79+5v87pEjR+Di4oIdO3ZApVLBycnJoN81hPQAqm9FwyVL\nlvDRGiVpTKhLo66/N6GenZ3NU2E6ODjwdqTEanq66KadbKrdfvXVV3puULdu3TLZvpQlQDfuRHIx\nUvrY8/LymnQRsLe3h6+vr1GjgoYIdSnjCGM1/ramZplpikePHvEZkfqOXRLqko98c9BdHKRPnz7Q\narWoqKhAUlISBg4cyAXj+PFb7ZUFAAAR1ElEQVTj9V40kpCXc6Rq69atvE198803ePDggd7neXl5\nOHTokF6aQmtra4NS6mVlZXFXlqYWg6m9bcCAAdiwYYOsvru6XL58ma/MaWtrCyJCmzZt4O/vz0tt\nke7o6Giw/3F9JCQkYOfOnXqzcdKiP+Z2X5NS20luKbNmzZIli5AuVVVVGDp0KBirSU24bNkyvc+j\no6Nx9epVWW1K716pY7t48WLugnLmzBn+WWBgoGIxVJId3fYszSrpFt0Ujs0R6vWtMaFb/Pz8sH79\nehQUFNRblBg8fPToEcLDw/X8wp2dnfUWlaqqqkJWVhZWr15tVIYuiczMTNjY2MDGxga+vr5YtGgR\nPv30U14WL15c7zn6XQj10NBQuLu749dffzV5Xzt37oSfnx8PXqmqqsLx48fRqVMndOrUCS4uLrLk\nTs3JyYGjoyPCw8ObzJX5z3/+k79oXF1dTQ6akRaTqL1se3p6OoKDg8FYTQ5bJdEV6roP+s8//5yn\nSLSzs8Pu3bv18jArhdJC/ZdffkH37t1BRHB2duZBjeZAypXPWM2qZ/Vx4MABTJ06FY6OjnBxccGS\nJUuwZMkSoxbRqk1GRgaio6MRGhqKxMRELF68mMegKBEApkt0dDS8vb0xfvz4em2dOHGC5/s25noc\nPnyYu5HourOUlpYiPj4e8fHxfJ2AxYsXy7pgWENIAasNzQxKq15+9NFHzZ4V1BXq9aVm++GHH9C1\na1e+xoVUF7ldX4CalKa67iyM1cS7SEU3FkIqtdeiMITaK5AmJSUhKCgIK1eu1NtmihA2lrfeegv9\n+vVrUPh4eXnBy8vL5JSrGo2GzxIsX74cGzdu5K5PSjJ16lRERkYiJSUFZWVluH79ut4IaEBAgNHu\np42Rm5vLbZw6darOCsIhISHQaDSypLLNy8tDXFwc3NzcMGfOHMyZM6eOZ8DXX38NKysr7NmzB2vW\nrIFGo0F8fLwiLkdSh6GhmaLa25oj1Lt16wZfX1+MHj0aJ06c0JsVYYzJ3vkxFEmf6sYkhIeH4623\n3sKPP/7IF6kLCAjAnTt3jLaTmZlp1EyCNLDX0MxVixbqMTExiImJkTV7xOPHj9GjRw8MGDAAKSkp\nmDlzJogIPj4+8PHxwY0bN2SxAwBjx47lda9PrJeWluLYsWN8iql169bIyckx2W7fvn3h4+ODvLw8\npKenIz09Hdu3b+cvPS8vL8WDSXWFeo8ePZCcnIx58+bB3d0djDFYW1vXmc5VEiWFekVFBUaPHs1F\nurl9HaOjo7lLU+vWrTF8+HAsX74cy5cvR2xsLDw8PGBlZcVHYY311zaUgQMHgojg6+urqB3g/1y5\nGKsJTpKmsTdv3ozNmzcjMDCQf25sx6lbt25gjCEsLAzffPMNkpOT9fy33dzczNLZlAgMDGx0FdK2\nbdvCx8cHz549w7Nnz5q176qqKvj6+sLX1xfjxo3jo0ulpaVITEzknRIvLy+cPn0aVVVV2LVrF9q1\na4d27dqZfGy12blzJ5+haaxYW1tj7ty5eP78uex1sDTZ2dlYt25dvaWpaXZDkRaM69ChA3bs2MET\nPhjiRmQsH374IV+xmjGmN1MgdQRNWTCsMSRXtY4dO9arXUJCQsCYPAseTZ06FSEhIQ0OilRVVaF/\n//5wcXHh265cuYJTp04pkju+qKgIS5cubXAWSfffo0aNapbb0YMHD3j2r2fPnuGNN94AYwzt27fH\n0qVLjVq0UU5KSkowaNAgvQ6JnZ0d//8777xj0v6fPHmCgIAABAQEGCzSAwMD+YBlQzNHLVKoFxQU\ncOFDVLNKmpxTX48fP8aIESNARHBxccGKFSuMeqk1xZMnT9C+fXsQ1SwxPWzYMMTExGDatGmYNm0a\n3N3deR1mzpwJtVotS+R5VFQUbGxsoNFo+JQ0EaFt27aYMmWKYjMfulRUVPCFaGqXXr16KfIAaowN\nGzYoJtSlxbiICF27dsXMmTN56dOnD+zs7KBWq6HRaPhDTE7u3LmDO3fu6D1o61uZ1NXVFbGxsbLb\nr420+qrSvvlAjd9n7XRv9RU7Ozvcvn3bKBvJycl85ULdIvmAG7tfY/H19YVKpcLmzZv1pmnz8/MR\nHBwMa2trbNy40ej9x8bGIjY2FowxhISEIDU1lft0Sx3v2n60kk+xEly4cKGOe5VumTx5stHZsQQ1\naLVadO3aFYwxLFy4EG3atAFjDKtXr1bM5vjx4+tdVZgxhpMnTxoURGwsUge+oWBguYT65MmT4evr\n26iL4apVq0BkeNpouZAWPnJzc6szou7j42NQdrzGOHDgAN+fUplsjEGr1WLPnj08Bkaqo1wJH6Q4\nBEPEekxMDHJzc5vcZ4sT6vfv3+eZSUJDQxEaGqqYL6A50Gq1SExMRGRkJA/kkHyYJkyYgK+++grF\nxcWorKxERkYGUlJSTJ7uSktLw8CBA6HRaHh57bXXZFsN01AqKyuxZcsWjBs3Dn379kVMTAz27dtn\n9l51fn4+OnbsyF/stV2CTGXQoEFNjvip1Wp07dpV0WuwcuXKOkLd1dUVs2fPxqJFixRJEVibp0+f\n8qxO5lrVLycnB6mpqdytq75iaucsOzsb8+bNw7x58xAaGoqEhIRmL/kuF+fOnePXd/78+Zg3bx7G\njBnTaIBpc5BE0sSJE+ucx/79+8s+oGEIVVVVSElJQVxcHFasWIEVK1Zg48aN0Gq1iscE/FG4desW\nH9RhjKFdu3ayxLA0hiSIpeLi4oJ58+ahvLxc0fiW48ePg7GG0xSnpaVh6dKlJnc+J0yY0Ghw+ePH\nj+Hj44PAwECTfKNNQYrXGDBgAC9yaK4BAwbA2toaMTExZl8J3FA+/vhjLFy4EFOnTkVaWpqs+37w\n4AHefvtt+Pn56bVxf39/vP3229i7d6/BA9CGaG+xMqlAIBAIBAKBQNACYfn5+TD0y87OzkYbunr1\nKg0ZMoQePXpEUVFRlJiYSERETk5ORu9TICAiCgkJobS0NCIievLkCbm6usq275ycHPr000/p0qVL\ndPDgQerUqRMFBwcTEVGrVq2oQ4cONHz4cHrppZdks9lSuX//PnXv3p0GDRpEn3zyCbVu3dpstktL\nS+n48eNERHTixAk6evQoZWRk0MyZM2nmzJnUvXt3s9VFSZ4/f05BQUF0584dAkCMMf7Zu+++SzNm\nzJC1fQv+OFy8eJH+/Oc/k6urK12+fJm8vb0VtXfkyBGKiIggIqJevXrRu+++S4MGDVLUpsA8hIaG\nUk5ODmVkZFi6Kv/xPHv2rMnvmE2oCwRKsXbtWlq8eDERyS/UBQKBQCAQCJTAEKFuZYZ6CASKMnbs\nWPrf//1fIiKysbGxcG0EAoFAIBAI5EGMqAsEAoFAIBAIBGZG9hH16upqqq6uNrpCAoFAIBAIBALB\nHx2VyrB8Ls0S6s+fPzeqMgKBQCAQCAQCgaB5iPSMAoFAIBAIBAJBC0QIdYFAIBAIBAKBoAUihLpA\nIBAIBAKBQNACEUJdIBAIBAKBQCBogQihLhAIBAKBQCAQtECEUBcIBAKBQCAQCFogigt1lpVFL4we\nTU4+PuTo50fqRYuIKiuVNktERHbTp5Nj587k5O1NDn/+M1nv2GEWu+zpU3phwgRy+tOfyLFrV7Le\nu9csdu0jIsjJw4OcvLzIycuLHIKCftd2bT75hOxDQ8nJ3Z3sYmLMYlPCUu3aUm1aQvXLL+Tk4UF2\n06ebx96tW2Q/YgQ5tWtHDi+9RFaHDpnFLpHlzrWwa6Y2XVZGdrNnk2PXruTUti059O1LVsePm8f2\nvzH3/WSpc22pd4SlntN/uHeTBe8lS55rc91Pigt1u4ULCW3aUMGtW1R49ixZ/fAD2WzZorRZIiIq\nmz+fnl+9SgX371Pxrl2kfvddUl2+rLhd9cKFBBsbKrh9m4o//ZTs/vpXUt28qbhdIqKSdeuoICeH\nCnJyqDA93Sw2LWW3WqOhsoULqXziRLPY08VS7dpSbVpCvXAhVfXsaR5jlZX0wuuvU8XQoVSQmUkl\nGzbQCzNmkOrOHbOYt9S5FnbN1KYrK6nay4sKv/6aCu7do9KlS+mF6GhiWVnK2/43Zr2fyLLPD0u8\nIyz1nP7DvZsseC9Z8lyb635SXKirsrKoIjKSSK0meHhQ5V/+Qqqff1baLBERVQcEENna1vyHMSLG\nqFVmprJGi4rI+uBBKluyhMjBgapCQqgiPJys9+xR1u4fkMqRI6ly+HCCq6vZbVuqXVukTf8b69RU\ngrMzVfbvbxZ7qtu3SfXwIZXPmkXUqhVVDRhAlb17k/Xu3Waxb6lzLeyaqU3b21NZXBzBx4dIpaLK\n8HCqbteOWplJuJr7fiKy7PPDEljqOf2HezdZ8F6y5Lk21/2kuFAvj4kh69RUouJiYg8ekNWJE1T5\nl78obZaj/utfycnTkxxffpng4UEVgwcrak915w6RlRVVd+zIt1V160atzDSirn7nHXLs0IHshw6l\nVmfPmsWmJe1aCku2a3O3aSIiKigg2/feo9KVK5W31RiA2e4lIguda2HXbHZ1YVotqX75peblqzQW\nvJ8sdo0t8I6wtP6wBC3hmM16L1kYc9xPigv1yv/3/6jVzz+Tk7c3Ob34IlUFBlLl8OFKm+WUfvAB\nFWRnU+GRI1QxYsT/9X4UghUVERwd9bbByYlYYaGidomISt95h55fvkzPb96k8kmTyH78eFKZYbTE\nUnYtiSXbtbnbNBGReuVKKn/jDYKXl+K2JKo7dSK0aUM2H31EVFFBVqdOkdUPPxCVlJitDpY418Ku\n+exyKirohWnTqHz8eKr281PcnCXuJwlLnGtLvSMsrT8sgcWP2cz3kqUxx/2krFCvrib7//5vqhgx\nggoePKCCX38llp9P6vh4Rc3WoVUrqgoJIfbgAdkkJytqCvb2xJ4/19vGCgoIDg6K2iUiqgoKInJ0\nJLK1pYrXX6fK3r3J6ptvfrd2LUZLaNdmbNOqq1fJ6rvvqPx//kdRO3WwtqainTvJ+tgxcvTzI5u/\n/Y0qIiMJf/qTeethxnMt7FrAbnU12c2YQbCxodJ16xQ3Z7H7SRczn2uLvCNawnPa3Fj6mM18L7UY\nFL6fFBXq7OlTUmVnU9m0aUS2tgRXVyqfMMHskfW8PpWVivfiqzt2JKqsJNUvv/BtrX76iaosMQXE\nGBHwx7FrJlpSuzZHm7b6/ntS3btHjl27kqOfH9n+7W9kffAgOZjBt7a6a1cqOnyYnmdmUvG+faS6\ne5eq/vxnxe3WhznOtbBrZrsA2c2eTSqtlop37CCytlbcpCXvp9pY6hqb4x3Rkp7T5sKix2yBe6ml\nodT9pKhQx3/9F1X7+JDt1q016YHy88lm1y6q6tJFSbNERMQeP67x0yosJKqqIquTJ8k6NZUqBwxQ\n1rC9PVWMGEG2771HVFRErdLSyPrIEaoYO1ZZu/n5ZHXyJFFpKVFlJVl/8QVZnTtHlYMG/T7tEtW0\nqdJSoqqqmvLvOiiNpdq1pdp0+eTJ9PzSJSo8e5YKz56l8uhoqhwyhIr27VPULhGR6qefaq5rcTHZ\nJCbWBJe+/rridi11roVdMz2n/416wQJS3b5NRbt3E9nZmcWmpe4ni51rC70jLKk//mjvJiLL3EtE\nZLFzbc77ieXn5yvarVVdvUp2cXHU6qefCK1aUWX//lS6di3B3V1Js8Ryc+mFqChq9dNPRABVe3tT\n2YwZVDFpkqJ2iWp6tXazZpHVt98SXF2pND6eKkaPVtZmbi69MHo0tcrIIFKpqMrPj8qWLKHKsLDf\npV0iIttVq0i9Zo3ettLFi6ksLk5x25Zo15Zs07rYrlpFqsxMKvnkE8VtqZctI5sdO4gqK6kyJIRK\n166l6g4dFLdrqXMt7JrxOX3vHjl1706wtSWysuLbSz78kCrGjFHcvoS57ieLXmMLvSMspT/+cO8m\nC95LljrX5ryfFBfqAoFAIBAIBAKBoPkonvVFIBAIBAKBQCAQNB8h1AUCgUAgEAgEghaIEOoCgUAg\nEAgEAkELRAh1gUAgEAgEAoGgBSKEukAgEAgEAoFA0AIRQl0gEAgEAoFAIGiBCKEuEAgEAoFAIBC0\nQIRQFwgEAoFAIBAIWiBCqAsEAoFAIBAIBC2Q/w8aktc/STLWBgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p7mcuLi5st2WxnDx5UrL1CeHh4XBwcICJiQlzusyaNUuScwM1ccTiJlqCIMDE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\n",
            "text/plain": [
              "<Figure size 936x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SVY1pBg5ydH-"
      },
      "source": [
        "## License"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hleIN5-pcr0N"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "author: Martin Gorner<br>\n",
        "twitter: @martin_gorner\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    http://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "This is not an official Google product but sample code provided for an educational purpose\n"
      ]
    }
  ]
}